




'''simple docstring'''


import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase_					=   "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase_					=   direct_transformers_import(PATH_TO_TRANSFORMERS)

UpperCAmelCase_					=   transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCAmelCase_					=   {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				str					,       a_						:				List[str]					,       a_						:				Union[str, Any]      ):
  __a								=       False
  for attribute in attributes:
    for modeling_source in source_strings:
      # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
      if (
          f"config.{attribute}" in modeling_source
          or f"getattr(config, \"{attribute}\"" in modeling_source
          or f"getattr(self.config, \"{attribute}\"" in modeling_source
      ):
        __a								=       True
      # Deal with multi-line cases
      elif (
          re.search(
              Rf"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""					,       a_					,       )
          is not None
      ):
        __a								=       True
      # `SequenceSummary` is called with `SequenceSummary(config)`
      elif attribute in [
          "summary_type",
          "summary_use_proj",
          "summary_activation",
          "summary_last_dropout",
          "summary_proj_to_labels",
          "summary_first_dropout",
      ]:
        if "SequenceSummary" in modeling_source:
          __a								=       True
      if attribute_used:
        break
    if attribute_used:
      break

    # common and important attributes, even if they do not always appear in the modeling files
  __a								=       [
      'bos_index',
      'eos_index',
      'pad_index',
      'unk_index',
      'mask_index',
      'image_size',
      'use_cache',
      'out_features',
      'out_indices',
  ]
  __a								=       ['encoder_no_repeat_ngram_size']

  # Special cases to be allowed
  __a								=       True
  if not attribute_used:
    __a								=       False
    for attribute in attributes:
      # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
      if attribute in ["is_encoder_decoder"] and default_value is True:
        __a								=       True
      elif attribute in ["tie_word_embeddings"] and default_value is False:
        __a								=       True

      # Allow cases without checking the default value in the configuration class
      elif attribute in attributes_to_allow + attributes_used_in_generation:
        __a								=       True
      elif attribute.endswith('_token_id'      ):
        __a								=       True

      # configuration class specific cases
      if not case_allowed:
        __a								=       SPECIAL_CASES_TO_ALLOW.get(config_class.__name__					,       []      )
        __a								=       allowed_cases is True or attribute in allowed_cases

  return attribute_used or case_allowed


def     SCREAMING_SNAKE_CASE							(   a_						:				Any      ):
  __a								=       dict(inspect.signature(config_class.__init__      ).parameters      )
  __a								=       [x for x in list(signature.keys()      ) if x not in ['self', 'kwargs']]
  __a								=       [signature[param].default for param in parameter_names]

  # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
  # as one variant is used, the test should pass
  __a								=       {}
  if len(config_class.attribute_map      ) > 0:
    __a								=       {v: k for k, v in config_class.attribute_map.items()}

  # Get the path to modeling source files
  __a								=       inspect.getsourcefile(a_      )
  __a								=       os.path.dirname(a_      )
  # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
  __a								=       [os.path.join(a_					,       a_      ) for fn in os.listdir(a_      ) if fn.startswith('modeling_'      )]

  # Get the source code strings
  __a								=       []
  for path in modeling_paths:
    if os.path.isfile(a_      ):
      with open(a_      ) as fp:
        modeling_sources.append(fp.read()      )

  __a								=       []
  for config_param, default_value in zip(a_					,       a_      ):
    # `attributes` here is all the variant names for `config_param`
    __a								=       [config_param]
    # some configuration classes have non-empty `attribute_map`, and both names could be used in the
    # corresponding modeling files. As long as one of them appears, it is fine.
    if config_param in reversed_attribute_map:
      attributes.append(reversed_attribute_map[config_param]      )

    if not check_attribute_being_used(a_					,       a_					,       a_					,       a_      ):
      unused_attributes.append(attributes[0]      )

  return sorted(a_      )


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       {}
  for _config_class in list(CONFIG_MAPPING.values()      ):
    # Skip deprecated models
    if "models.deprecated" in _config_class.__module__:
      continue
    # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
    __a								=       [
        cls
        for name, cls in inspect.getmembers(
            inspect.getmodule(_config_class      )					,       lambda a_      : inspect.isclass(a_      )
            and issubclass(a_					,       a_      )
            and inspect.getmodule(a_      ) == inspect.getmodule(_config_class      )					,       )
    ]
    for config_class in config_classes_in_module:
      __a								=       check_config_attributes_being_used(a_      )
      if len(a_      ) > 0:
        __a								=       unused_attributes

  if len(a_      ) > 0:
    __a								=       'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
    for name, attributes in configs_with_unused_attributes.items():
      error += f"{name}: {attributes}\n"

    raise ValueError(a_      )


if __name__ == "__main__":
  check_config_attributes()









'''simple docstring'''


import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """segformer"""







   def __init__(						self   ,				UpperCamelCase=3   ,				UpperCamelCase=4   ,				UpperCamelCase=[2, 2, 2, 2]   ,				UpperCamelCase=[8, 4, 2, 1]   ,				UpperCamelCase=[32, 64, 160, 256]   ,				UpperCamelCase=[7, 3, 3, 3]   ,				UpperCamelCase=[4, 2, 2, 2]   ,				UpperCamelCase=[1, 2, 5, 8]   ,				UpperCamelCase=[4, 4, 4, 4]   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.02   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-6   ,				UpperCamelCase=256   ,				UpperCamelCase=255   ,				**UpperCamelCase   ,				)					->     int:
     super().__init__(**UpperCamelCase   )

     if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
       warnings.warn(
           'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
           ' removed, as the behaviour will default to that of reshape_last_stage = True.'   ,				UpperCamelCase   ,				)

     __a								=       num_channels
     __a								=       num_encoder_blocks
     __a								=       depths
     __a								=       sr_ratios
     __a								=       hidden_sizes
     __a								=       patch_sizes
     __a								=       strides
     __a								=       mlp_ratios
     __a								=       num_attention_heads
     __a								=       hidden_act
     __a								=       hidden_dropout_prob
     __a								=       attention_probs_dropout_prob
     __a								=       classifier_dropout_prob
     __a								=       initializer_range
     __a								=       drop_path_rate
     __a								=       layer_norm_eps
     __a								=       decoder_hidden_size
     __a								=       kwargs.get('reshape_last_stage'   ,				UpperCamelCase   )
     __a								=       semantic_loss_ignore_index





class  __lowercase    (	__magic_name__			):
   _a    						=   version.parse("""1.11"""			)







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]   )







   @property
   def 		UpperCamelCase__      (						self   )					->     float:
     return 1e-4







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return 12



